Biology subjects

Rasche, H.

Publications and source records attributed to Rasche, H..

6 recordsLinked to original sources

bioRxiv2022

Galaxy Training: A Powerful Framework for Teaching!

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis and stewardship are still rarely taught in life science educational programs [1], resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org); an open access, community-driven framework for the collection of FAIR training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform [2]. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.

bioinformatics↗

bioRxiv2022

Planemo: a command-line toolkit for developing, deploying, and executing scientific data analyses

There are thousands of well-maintained high-quality open-source software utilities for all aspects of scientific data analysis. For over a decade, the Galaxy Project has been providing computational infrastructure and a unified user interface for these tools to make them accessible to a wide range of researchers. In order to streamline the process of integrating tools and constructing workflows as much as possible, we have developed Planemo, a software development kit for tool and workflow developers and Galaxy power users. Here we outline Planemos implementation and describe its broad range of functionality for designing, testing and executing Galaxy tools, workflows and training material. In addition, we discuss the philosophy underlying Galaxy tool and workflow development, and how Planemo encourages the use of development best practices, such as test-driven development, by its users, including those who are not professional software developers. Planemo is a mature project widely used within the Galaxy community which has been downloaded over 80,000 times.

bioinformatics↗

bioRxiv2020

Expanding the Galaxy's reference data

SummaryProperly and effectively managing reference datasets is an important task for many bioinformatics analyses. Refgenie is a reference asset management system that allows to easily organize, retrieve, and share such datasets. Here, we describe the integration of refgenie into the Galaxy platform. Server administrators are able to configure Galaxy to make use of reference datasets made available on a refgenie instance. Additionally, a Galaxy Data Manager tool has been developed to provide a graphical interface to refgenies remote reference retrieval functionality. A large collection of reference datasets has also been made available using the CVMFS repository from GalaxyProject.org, with mirrors across the United States, Canada, Europe, and Australia, enabling easy use outside of Galaxy. Availability and implementationThe ability of Galaxy to use refgenie assets was added to the core Galaxy framework in version 20.05, which is available from https://github.com/galaxyproject/galaxy under the Academic Free License version 3.0. The refgenie Data Manager tool can be installed via the Galaxy ToolShed, with source code managed at https://github.com/BlankenbergLab/galaxy-tools-blankenberg/tree/main/data_managers/data_manager_refgenie_pull and released using an MIT license.

bioinformatics↗

bioRxiv2020

Training Infrastructure as a Service

BackgroundHands-on training, whether it is in Bioinformatics or other scientific domains, requires significant resources and knowledge to setup and run. Trainers must have access to infrastructure that can support the sudden spike in usage, with classes of 30 or more trainees simultaneously running resource intensive tools. For efficient classes, the jobs must run quickly, without queuing delays, lest they disrupt the timetable set out for the class. Often times this is achieved via running on a private server where there is no contention for the queue, and therefore no or minimal waiting time. However, this requires the teacher or trainer to have the technical knowledge to manage compute infrastructure, in addition to their didactic responsibilities. This presents significant burdens to potential training events, in terms of infrastructure cost, person-hours of preparation, technical knowledge, and available staff to manage such events. FindingsGalaxy Europe has developed Training Infrastructure as a Service (TIaaS) which we provide to the scientific commnuity as a service built on top of the Galaxy Platform. Training event organisers request a training and Galaxy administrators can allocate private queues specifically for the training. Trainees are transparently placed in a private queue where their jobs run without delay. Trainers access the dashboard of the TIaaS Service and can remotely follow the progress of their trainees without in-person interactions. ConclusionsTIaaS on Galaxy Europe provides reusable and fast infrastructure for Galaxy training. The instructor dashboard provides visibility into class progress, making in-person trainings more efficient and remote training possible. In the past 24 months, > 110 trainings with over 3000 trainees have used this infrastructure for training, across scientific domains, all enjoying the accessibility and reproducibility of Galaxy for training the next generation of bioinformaticians. TIaaS itself is an extension to Galaxy which can be deployed by any Galaxy administrator to provide similar benefits for their users. https://galaxyproject.eu/tiaas

bioinformatics↗

bioRxiv2020

User-friendly, scalable tools and workflows for single-cell analysis

Single-cell RNA-Seq (scRNA-Seq) data analysis requires expertise in command-line tools, programming languages and scaling on compute infrastructure. As scRNA-Seq becomes widespread, computational pipelines need to be more accessible, simpler and scalable. We introduce an interactive analysis environment for scRNA-Seq, based on Galaxy, with ~70 functions from major single-cell analysis tools, which can be run on compute clusters, cloud providers or single machines, to bring compute to the data in scRNA-Seq.

bioinformatics↗

bioRxiv2019

Apollo: Democratizing genome annotation

Genome annotation is the process of identifying the location and function of a genomes encoded features. Improving the biological accuracy of annotation is a complex and iterative process requiring researchers to review and incorporate multiple sources of information such as transcriptome alignments, predictive models based on sequence profiles, and comparisons to features found in related organisms. Because rapidly decreasing costs are enabling an ever-growing number of scientists to incorporate sequencing as a routine laboratory technique, there is widespread demand for tools that can assist in the deliberative analytical review of genomic information. To this end, Apollo is an open source software package that enables researchers to efficiently inspect and refine the precise structure and role of genomic features in a graphical browser-based platform.\n\nIn this paper we first outline some of Apollos newer user interface features, which were driven by the needs of this expanding genomics community. These include support for real-time collaboration, allowing distributed users to simultaneously edit the same encoded features while also instantly seeing the updates made by other researchers on the same region in a manner similar to Google Docs. Its technical architecture enables Apollo to be integrated into multiple existing genomic analysis pipelines and heterogeneous laboratory workflow platforms. Finally, we consider the implications that Apollo and related applications may have on how the results of genome research are published and made accessible. O_LISource: https://github.com/GMOD/Apollo\nC_LIO_LILicense (BSD-3): https://github.com/GMOD/Apollo/blob/master/LICENSE.md\nC_LIO_LIDocker: https://hub.docker.com/r/gmod/apollo/tags/, https://github.com/GMOD/docker-apollo\nC_LIO_LIRequirements: JDK 1.8, Node v6.0+\nC_LIO_LIUser guide: http://genomearchitect.org; technical guide: http://genomearchitect.readthedocs.io/en/latest/\nC_LIO_LIMailing list: apollo@lists.lbl.gov\nC_LI

bioinformatics↗